Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches

ISBN-13: 9780128025819 / Angielski / Twarda / 2015 / 542 str.

Zhou, Kevin

Opis

This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn:

• Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects
• Methods and theories for medical image recognition, segmentation and parsing of multiple objects
• Efficient and effective machine learning solutions based on big datasets
• Selected applications of medical image parsing using proven algorithms

• Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects
• Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets
• Includes algorithms for recognizing and parsing of known anatomies for practical applications

Spis treści

PrefaceChapter 1 Introduction to Medical Image Recognition and ParsingChapter 2 Discriminative Anatomy Detection: Classification vs. RegressionChapter 3: Information Theoretic Landmark DetectionChapter 4: Submodular Landmark DetectionChapter 5: Random Forests for Anatomy Recognition Chapter 6: Integrated Detection Network for Multiple Object RecognitionChapter 7: Optimal Graph-Based Method for Multi-Object Segmentation Chapter 8: Parsing of Multiple Organs Using Learning Method and Level SetsChapter 9: Context Integration for Rapid Multiple Organ ParsingChapter 10: Multi-Atlas Methods and Label FusionChapter 11: Multi-Compartment Segmentation Framework Chapter 12: Deformable Segmentation via Sparse Representation and Dictionary Learning Chapter 13: Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection Chapter 14: Whole Brain Anatomical Structure Parsing Chapter 15: Aortic and Mitral Valve Segmentation Chapter 16: Parsing of Heart, Chambers and Coronary Vessels Chapter 17: Spine Segmentation Chapter 18: Parsing of Rib and Knee BonesChapter 19: Lymph Node Segmentation Chapter 20: Polyp Segmentation from CT Colonoscopy

O autorze

S. Kevin Zhou, Ph.D. is currently a Principal Key Expert Scientist at Siemens Healthcare Technology Center, leading a team of full time research scientists and students dedicated to researching and developing innovative solutions for medical and industrial imaging products. His research interests lie in computer vision and machine/deep learning and their applications to medical image analysis, face recognition and modeling, etc. He has published over 150 book chapters and peer-reviewed journal and conference papers, registered over 250 patents and inventions, written two research monographs, and edited three books. He has won multiple technology, patent and product awards, including R&D 100 Award and Siemens Inventor of the Year. He is an editorial board member for Medical Image Analysis journal and a fellow of American Institute of Medical and Biological Engineering (AIMBE).